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Probabilistic Planning for Robotics with ROSPlan

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Towards Autonomous Robotic Systems (TAROS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11649))

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Abstract

Probabilistic planning is very useful for handling uncertainty in planning tasks to be carried out by robots. ROSPlan is a framework for task planning in the Robot Operating System (ROS), but until now it has not been possible to use probabilistic planners within the framework. This systems paper presents a standardized integration of probabilistic planners into ROSPlan that allows for reasoning with non-deterministic effects and is agnostic to the probabilistic planner used. We instantiate the framework in a system for the case of a mobile robot performing tasks indoors, where probabilistic plans are generated and executed by the PROST planner. We evaluate the effectiveness of the proposed approach in a real-world robotic scenario.

This work has been supported by the ERC project Clothilde (ERC-2016-ADG-741930), the HuMoUR project (Spanish Ministry of Science and Innovation TIN2017-90086-R) and by the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (MDM-2016-0656). G. Canal is also supported by the Spanish Ministry of Education, Culture and Sport by the FPU15/00504 doctoral grant and the mobility grant EST17/00371. The research from KCL was partly supported by Korea Evaluation Institute of Industrial Technology (KEIT) funded by the Ministry of Trade, Industry & Energy (MOTIE) (No. 1415158956).

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Notes

  1. 1.

    The source code of the elements described in this paper can be found in the main ROSPlan repository https://github.com/KCL-Planning/ROSPlan.

  2. 2.

    Both PDDL and RDDL domains can be found here: https://github.com/m312z/KCL-Turtlebot/tree/master/domains.

  3. 3.

    A video demonstration of this setup can be found in https://youtu.be/aozTz4Ex7PI.

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Author information

Authors and Affiliations

  1. Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain

    Gerard Canal, Guillem Alenyà & Carme Torras

  2. Department of Computer Science, King’s College London, London, UK

    Michael Cashmore, Senka Krivić & Daniele Magazzeni

Authors
  1. Gerard Canal
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  2. Michael Cashmore
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  3. Senka Krivić
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  4. Guillem Alenyà
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  5. Daniele Magazzeni
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  6. Carme Torras
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Corresponding author

Correspondence to Gerard Canal .

Editor information

Editors and Affiliations

  1. Queen Mary University of London, London, UK

    Kaspar Althoefer

  2. Queen Mary University of London, London, UK

    Jelizaveta Konstantinova

  3. Queen Mary University of London, London, UK

    Ketao Zhang

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Canal, G., Cashmore, M., Krivić, S., Alenyà, G., Magazzeni, D., Torras, C. (2019). Probabilistic Planning for Robotics with ROSPlan. In: Althoefer, K., Konstantinova, J., Zhang, K. (eds) Towards Autonomous Robotic Systems. TAROS 2019. Lecture Notes in Computer Science(), vol 11649. Springer, Cham. https://doi.org/10.1007/978-3-030-23807-0_20

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  • DOI: https://doi.org/10.1007/978-3-030-23807-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23806-3

  • Online ISBN: 978-3-030-23807-0

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